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Book Recommendation System using Data Mining for the University of Hong Kong Libraries Discuss this presentation on facebook

RAJAGOPAL, Sandhya (Faculty of Education, University of Hong Kong)

Time:15 June 2012, Friday, 4:00 - 4:20 pm
Venue:Room 8 & 9, Collaboration Zone, Level 3, Main Library, HKU
Sub-theme:Emerging technologies: cloud computing, mobile technologies, beyond Web 2.0 – changing affordances for emerging practices
Presentation format:Paper Presentations
Medium of instruction:English

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Abstract

This report describes the theoretical design of a Library Recommendation System, employing k-means clustering Data Mining algorithm, with subject headings of borrowed items as the basis for generating pertinent recommendations. Sample data from the University Of Hong Kong Libraries (HKUL) has been used in a Quantitative approach to study the existing Library Information System, Innopac. Data Warehousing and Data Mining (k-means clustering) techniques are discussed. The primary benefit of the system is higher quality of academic research ensuing from better search results. Personalization improves individual effectiveness of learners and overall, in better utilizing library resources.


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Last modified on 23 May 2012, 2:11 pm
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